Statistical procedures for mapping quantitative trait loci (QTL) have been extensively studied because they are the essential for improving the accuracy of genetic analyses. The objective of the present study was to examine QTL using multivariate methods, considering the principle of simple interval mapping. A microsatellite marker data set from a F2 population was simulated. It was assumed that the QTL control binomial and normal traits. In the normal case, five QTL mapping models were evaluated that had the following residual covariance structures: variance components (VC), compound symmetry (CS), unstructured (UN), banded main diagonal (UN1) and heterogeneous toeplitz. Akaike’s information criterion (AIC) was used to select the appropriate structure. In the binary case, the Generalized Estimating Equations (GEE) procedure was used. UN structure minimized the AIC value on the interval that indicated a higher probability of the QTL. In the binomial case, a non-independent working correlation matrix (WCM) was fitted (ρ = 0.47). In both cases, the additive effect of QTL was significant (p < 0.01), but dominance effects were not (p > 0.05). Thus, QTL mapping using a multivariate approach may be a useful tool for breeding programs that aim to improve quantitative traits that have phenotypic values that change over time.